Many serious diseases with high morbidity and mortality, including cancer and heart diseases, are very difficult to diagnose early and accurately. Current disease diagnosis technologies typically rely on macroscopic data and information such as body temperature, blood pressure, and scanned images of the body. To detect serious diseases such as cancer, many of the diagnosis apparatus commonly used today are based on imaging technologies, including x-ray, CT scan, and nuclear magnetic resonance (NMR). While they provide various degrees of usefulness in disease diagnosis, most of them cannot provide accurate, totally safe, and cost-effective diagnosis of such serious diseases as cancer at an early stage. Further, many of the existing diagnosis techniques and related apparatus are invasive and sometimes not readily accessible, especially in remote regions or rural areas.
Even the newly emerged DNA tests have not been proven effective in diagnosing a wide range of diseases in a rapid, reliable, accurate, and cost-effective manner. In recent years, there have been some efforts in using nano technologies for various biological applications, with most of the work focused on gene mapping and moderate developments in the field of disease detection. For instance, Pantel et al. discussed the use of a MicroEelectroMechanical Systems (MEMS) sensor for detecting cancer cells in blood and bone marrow in vitro (see, e.g., Klaus Pantel et al., Nature Reviews, 2008, 8, 329); Kubena et al. disclose in U.S. Pat. No. 6,922,118 the deployment of MEMS for detecting biological agents; and Weissman et al. disclose in U.S. Pat. No. 6,330,885 utilizing MEMS sensor for detecting accretion of biological matter.
However, to date, most of the above described technologies have been limited to isolated examples for sensing, using systems of relatively simple constructions and large dimensions but often with limited functions, and lack sensitivities and specificities. Further, some existing technologies utilizing nano-particles and biological approaches have the drawbacks of requiring complicated sample preparation procedures (such as using chemical or biological markers), difficulty in data interpretation, and too much reliance on visual and color change as means of diagnosis (which is subjective and of limited resolution), making them unsuitable for early stage disease detection, e.g., for such serious diseases as cancer.
These drawbacks call for novel solutions that not only overcome them but also bring enhanced accuracy, specificity, efficiency, non-invasiveness, and speed in early-stage disease detection at reduced costs.